Today more than ever, information plays an increasingly important role in the lives of individuals and companies. The Internet has transformed how goods and services are bought and sold between consumers, between businesses and consumers, and between businesses. In a macro sense, highly-competitive business environments cannot afford to squander any resources. Better examination of the data stored on systems, and the value of the information can be crucial to better align company strategies with greater business goals. In a micro sense, decisions by machine processes can impact the way a system reacts and/or a human interacts to handling data.
A basic premise is that information affects performance at least insofar as its accessibility is concerned. Accordingly, information has value because an entity (whether human or non-human) can typically take different actions depending on what is learned, thereby obtaining higher benefits or incurring lower costs as a result of knowing the information. In one example, accurate, timely, and relevant information saves transportation agencies both time and money through increased efficiency, improved productivity, and rapid deployment of innovations. In the realm of large government agencies, access to research results allows one agency to benefit from the experiences of other agencies and to avoid costly duplication of effort.
The vast amounts of information being stored on networks (e.g., the Internet) and computers are becoming more accessible to many different entities, including both machines and humans. However, because there is so much information available for searching, the search results are just as daunting to review for the desired information as the volumes of information from which the results were obtained.
Some conventional systems employ ranking systems (e.g., page ranking) that prioritize returned results to aid the user in reviewing the search results. However, the user is oftentimes still forced to sift through the long ordered lists of document snippets returned by the engines, which is time-consuming and inconvenient for identifying relevant topics inside the results. These ordered lists can be obtained from underlying processes that cluster or group results to provide some sort of prioritized list of likely results for the user. However, clustering has yet to be deployed on most major search engines. Accordingly, improved search methodologies are desired to provide not only more efficient searching but more effect searching, and moreover, not only at a high level, but in more focused regimes.